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A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects
Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden; Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden.
Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden; Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden; Department of Radiation Physics, Linköping University, Linköping, Sweden.
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2023 (English)In: Diabetology & Metabolic Syndrome, E-ISSN 1758-5996, Vol. 15, no 1, article id 250Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: The increased prevalence of insulin resistance is one of the major health risks in society today. Insulin resistance involves both short-term dynamics, such as altered meal responses, and long-term dynamics, such as the development of type 2 diabetes. Insulin resistance also occurs on different physiological levels, ranging from disease phenotypes to organ-organ communication and intracellular signaling. To better understand the progression of insulin resistance, an analysis method is needed that can combine different timescales and physiological levels. One such method is digital twins, consisting of combined mechanistic mathematical models. We have previously developed a model for short-term glucose homeostasis and intracellular insulin signaling, and there exist long-term weight regulation models. Herein, we combine these models into a first interconnected digital twin for the progression of insulin resistance in humans.

METHODS: The model is based on ordinary differential equations representing biochemical and physiological processes, in which unknown parameters were fitted to data using a MATLAB toolbox. RESULTS: The interconnected twin correctly predicts independent data from a weight increase study, both for weight-changes, fasting plasma insulin and glucose levels, and intracellular insulin signaling. Similarly, the model can predict independent weight-change data in a weight loss study with the weight loss drug topiramate. The model can also predict non-measured variables.

CONCLUSIONS: The model presented herein constitutes the basis for a new digital twin technology, which in the future could be used to aid medical pedagogy and increase motivation and compliance and thus aid in the prevention and treatment of insulin resistance.

Place, publisher, year, edition, pages
BioMed Central (BMC), 2023. Vol. 15, no 1, article id 250
Keywords [en]
Digital twin, Insulin resistance, Mathematical modelling
National Category
Endocrinology and Diabetes
Identifiers
URN: urn:nbn:se:oru:diva-110012DOI: 10.1186/s13098-023-01223-6ISI: 001113063500002PubMedID: 38044443OAI: oai:DiVA.org:oru-110012DiVA, id: diva2:1816565
Funder
Linköpings universitetSwedish Research Council, 2018–05418; 2018–03319; 2018–03391Swedish Foundation for Strategic Research, ITM17-0245Knut and Alice Wallenberg Foundation, 2020.0182Swedish Fund for Research Without Animal Experiments, F2019-0010Vinnova, 2020–04711
Note

Funding Agencies:

Linköping University

Swedish Research Council

CENIIT, Center for Industrial Information Technology,

The Swedish Foundation for Strategic Research

SciLifeLab National COVID-19 Research Program, financed by the Knut and Alice Wallenberg Foundation

The H2020 project PRECISE4Q, Personalised Medicine by Predictive Modelling in Stroke for better Quality of Life

The Swedish Fund for Research without Animal Experiments

ELLIIT, Excellence Center at Linköping – Lund in Information Technology

VINNOVA (VisualSweden) and VINNOVA together with MedTech4Health and SweLife

Available from: 2023-12-04 Created: 2023-12-04 Last updated: 2024-03-14Bibliographically approved

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Cedersund, Gunnar

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